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A light-robust-optimization model and an effective memetic algorithm for an open vehicle routing problem under uncertain travel times

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Abstract

This paper addresses an open vehicle routing problem with predetermined time windows under uncertain travel times (OVRP-UT). A novel light-robust-optimization model is proposed by integrating the goal programming formulations with set-based descriptions of the problem data, which can enable as many customers as possible to meet their demands within a group of predetermined time windows. An effective memetic algorithm (MA) is presented for solving the OVRP-UT model. We design a heuristic-based initialization mechanism to generate an initial population with a high level of quality and diversity. We design a timely-vertices based crossover operator and mutation operator to give birth to the offspring with high quality and good structure built in the search process. We provide a hybrid selection mechanism and a population updating strategy to remain the diversity of the population. We develop a self-adapted crossover and mutation rate to help the MA suit the different phases during the search process. A comprehensive simulation experiment based on the 320 benchmark instances demonstrates the effectiveness of the proposed algorithm.

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References

  1. Bertsimas DJ (1992) A vehicle routing problem with stochastic demand. Oper Res 40:574–585

    Article  MathSciNet  Google Scholar 

  2. Christiansen CH, Lysgaard J (2007) A branch-and-price algorithm for the capacitated vehicle routing problem with stochastic demands. Oper Res Lett 35(6):773–781

    Article  MathSciNet  Google Scholar 

  3. Laporte G, Louveaux F, Mercure H (1992) The vehicle routing problem with stochastic travel times. Transp Sci 26(3):161–170

    Article  Google Scholar 

  4. Oyola J, Arntzen H, Woodruff DL (1999) The stochastic vehicle routing problem, a literature review, parts I: models. Transp Res E-log 7:193–221

    Google Scholar 

  5. Sungur I, Ordóñez F, Dessouky M (2008) A robust optimization approach for the capacitated vehicle routing problem with demand uncertainty. IIE Trans 40(5):509–523

    Article  Google Scholar 

  6. Lee C, Lee K, Park S (2011) Robust vehicle routing problem with deadlines and travel time/demand uncertainty. J Oper Res Soc 63(9):1294–1306

    Article  Google Scholar 

  7. Schöbel A (2014) Generalized light robustness and the trade-off between robustness and nominal quality. Math Method Oper Res 80(2):161–191

    Article  MathSciNet  Google Scholar 

  8. Fischetti M, Monaci M (2009) Light robustness. Robust and online large-scale optimization, Springer, Berlin, pp 61–84

    Book  Google Scholar 

  9. Dimitris B, David BB, Constantine C (2011) Theory and applications of robust optimization. Siam Rev 53(3):464–501

    Article  MathSciNet  Google Scholar 

  10. Ben-Tal A, Nemirovski A (2000) Robust solutions of linear programming with uncertain data. Math Prog 88:411–424

    Article  MathSciNet  Google Scholar 

  11. Calvete HI, Galé C, Oliveros MJ, Sánchez-Valverde B (2007) A goal programming approach to vehicle routing problems with soft time windows. Eur J Oper Res 117(3):1720–1733

    Article  MathSciNet  Google Scholar 

  12. Ghoseiri K, Ghannadpour SF (2010) Multi-objective vehicle routing problem with time windows using goal programming and genetic algorithm. Appl Soft Comput 10(4):1096–1107

    Article  Google Scholar 

  13. Taillard É, Badeau P, Gendreau M, Guertin F, Potvin JY (1997) A tabu search heuristic for the vehicle routing problem with soft time windows. Transp Sci 31(2):170–186

    Article  Google Scholar 

  14. Sabar N R, Bhaskar A, Chung E, Turky A, Song A (2020) An adaptive memetic approach for heterogeneous vehicle routing problems with two-dimensional loading constraints. Swarm Evol Comput. 58:Article 100730

  15. Sethanan K, Jamru T (2020) Hybrid differential evolution algorithm and genetic operator for multi-trip vehicle routing problem with backhauls and heterogeneous fleet in the beverage logistics industry. Comput Ind Eng 146:Article 106571

  16. Molina J C, Salmeron J L, Eguia I (2020) An ACS-based memetic algorithm for the heterogeneous vehicle routing problem with time windows, Expert Syst Appl 1571: Article 113379

  17. Dong X-F, He S-P, Stojanovic V (2020) Robust fault detection filter design for a class of discrete-time conic-type nonlinear Markov jump systems with jump fault signals. IET Control Theory A. https://doi.org/10.1049/iet-cta.2019.1316

    Article  Google Scholar 

  18. Xiang Z, He S-P, Stojanovic V, Luan X-L, Fei L (2020) Finite-time asynchronous dissipative filtering of conic-type nonlinear markov jump systems. Sci China Inform Sci. https://doi.org/10.1007/s11432-020-2913-x

    Article  Google Scholar 

  19. Zhou L-H, Tao H-F, Paszke W, Stojanovic V, Yang H-Z (2020) PD-type iterative learning control for uncertain spatially interconnected systems. mathematics. https://doi.org/10.3390/math8091528

  20. Chen Z-Y, Zhao B-Y, Stojanovic V, Zhang Y-J, Zhang Z-Q (2020) Event-based fuzzy control for T-S fuzzy networked systems with various data missing. Neurocomputing 417(5):322–332

    Article  Google Scholar 

  21. Chrysanthos E-G, Panagiotis P, Repoussis CD, Tarantilis Wolfram W, Christodoulos A-F (2016) An adaptive memory programming framework for the robust capacitated vehicle routing problem. Transp Sci 50(4):1239–1260

    Article  Google Scholar 

  22. Montemanni R, Barta J, Mastrolilli M, Gambardella LM (2007) The robust traveling salesman problem with interval data. Transp Sci 41(3):366–381

    Article  Google Scholar 

  23. Cao E, Lai M, Yang H (2014) Open vehicle routing problem with demand uncertainty and its robust strategies. Expert Syst Appl 41(7):3569–3575

    Article  Google Scholar 

  24. Solano CE, Prins C, Santos AC (2015) Local search based meta-heuristics for the robust vehicle routing problem with discrete scenarios. Appl Soft Comput 32:518–531

    Article  Google Scholar 

  25. Braaten S, Gjønnes O, HvattumL M, Tirado G (2017) Heuristics for the robust vehicle routing problem with time windows. Expert Syst Appl 77:136–147

    Article  Google Scholar 

  26. Han J, Lee C, Park S (2013) A robust scenario approach for the vehicle routing problem with uncertain travel times. Transp Sci 48(3):373–390

    Article  Google Scholar 

  27. Lu D, Gzara F (2019) The robust vehicle routing problem with time windows: solution by branch and price and cut. Eur J Oper Res 275(3):925–938

    Article  MathSciNet  Google Scholar 

  28. Pedro M, Alfredo M, Jonathan DLV, Douglas A, Jacek G, Reinaldo M (2019) The robust vehicle routing problem with time windows: compact formulation and branch-price-and-cut method. Transp Sci 53(4):1043–1066

    Article  Google Scholar 

  29. De LVJ, Munari P, Morabito R (2018) Robust optimization for the vehicle routing problem with multiple deliverymen. Cent Eur J Oper Res 27:905–936

    MathSciNet  MATH  Google Scholar 

  30. Pureza V, Morabito R, Reimann M (2012) Vehicle routing with multiple deliverymen: modeling and heuristic approaches for the VRPTW. Eur J Oper Res 218(3):636–647

    Article  MathSciNet  Google Scholar 

  31. Sim K, Hart E (2016) A combined generative and selective hyper-heuristic for the vehicle routing problem//genetic & evolutionary computation conference. ACM 2016

  32. Bertsimas D, Sim M (2004) The price of robustness. Oper Res 52:35–53

    Article  MathSciNet  Google Scholar 

  33. Adulyasak YP (2015) Models and algorithms for stochastic and robust vehicle routing with deadlines. Transp Sci 50(2):608–626

    Article  Google Scholar 

  34. Harary F (1994) Graph theory. Addison-Wesley, Massachusetts, pp 40–41

    Google Scholar 

  35. Stojanovic V, Nedic N (2016) A nature inspired parameter tuning approach to cascade control for hydraulically driven parallel robot platform. J Optimiz Theory Appl 168:332–347

    Article  MathSciNet  Google Scholar 

  36. Stojanovic V, Nedic N, Dragan P, Ljubisa D, Vladimir D (2016) Application of cuckoo search algorithm to constrained control problem of a parallel robot platform. Int J Adv Manuf Tech 87:2497–2507

    Article  Google Scholar 

  37. Montgomery DC (2012) Design and analysis of experiments, 8 edn. Wiley, Boca Raton

  38. Pan Q-K, Gao L, Wang L, Liang J, Li X-Y (2019) Effective heuristics and metaheuristics to minimize total flowtime for the distributed permutation flowshop problem. Expert Syst Appl 124:309–324

    Article  Google Scholar 

  39. Pan Q-K, Gao L, Li X-Y, Gao K-Z (2017) Effective metaheuristics for scheduling a hybrid flowshop with sequence-dependent setup times. Appl Math Comput 303:89–112

    MathSciNet  MATH  Google Scholar 

  40. Solomon MM (1987) Algorithms for the vehicle routing and scheduling problems with time window constraints. Oper Res 35:254–265

    Article  MathSciNet  Google Scholar 

  41. Sun L (2018) A goal-robust-optimization approach for solving open vehicle routing problems with demand uncertainty. Wireless Pers Commun 103(1):1059–1075

    Article  Google Scholar 

  42. Wu L, Hifi M, Bederina H (2017) A new robust criterion for the vehicle routing problem with uncertain travel time. Comput Ind Eng 112:607–615

    Article  Google Scholar 

  43. Pan Q-K (2016) An effective co-evolutionary artificial bee colony algorithm for steelmaking-continuous casting scheduling. Euro J Oper Res 250:702–714

    Article  MathSciNet  Google Scholar 

  44. Meng T, Pan Q-K (2021) A distributed heterogeneous permutation flowshop scheduling problem with lot-streaming and carryover sequence-dependent setup time. Swarm Evol Comput 60:100804. https://doi.org/10.1016/j.swevo.2020.100804

    Article  Google Scholar 

  45. Huang J-P, Pan Q-K, Miao Z-H, Gao L (2021). Effective constructive heuristics and discrete bee colony optimization for distributed flowshop with setup times. Eng Appl Artif Intel 97: Article 104016

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Acknowledgements

This research is partially supported by the National Science Foundation of China 61973203 and 51575212, and Shanghai Key Laboratory of Power station Automation Technology.

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Correspondence to Quan-ke Pan.

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Sun, L., Pan, Qk., Jing, XL. et al. A light-robust-optimization model and an effective memetic algorithm for an open vehicle routing problem under uncertain travel times. Memetic Comp. 13, 149–167 (2021). https://doi.org/10.1007/s12293-020-00322-5

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